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DOI: 10.4338/ACI-2014-10-RA-0088
A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time
Correspondence to:
Publikationsverlauf
received:
09. Oktober 2014
accepted:
09. April 2015
Publikationsdatum:
19. Dezember 2017 (online)
Summary
Objective: To save time, healthcare providers frequently use abbreviations while authoring clinical documents. Nevertheless, abbreviations that authors deem unambiguous often confuse other readers, including clinicians, patients, and natural language processing (NLP) systems. Most current clinical NLP systems “post-process” notes long after clinicians enter them into electronic health record systems (EHRs). Such post-processing cannot guarantee 100% accuracy in abbreviation identification and disambiguation, since multiple alternative interpretations exist.
Methods: Authors describe a prototype system for real-time Clinical Abbreviation Recognition and Disambiguation (rCARD) – i.e., a system that interacts with authors during note generation to verify correct abbreviation senses. The rCARD system design anticipates future integration with web-based clinical documentation systems to improve quality of healthcare records. When clinicians enter documents, rCARD will automatically recognize each abbreviation. For abbreviations with multiple possible senses, rCARD will show a ranked list of possible meanings with the best predicted sense at the top. The prototype application embodies three word sense disambiguation (WSD) methods to predict the correct senses of abbreviations. We then conducted three experments to evaluate rCARD, including 1) a performance evaluation of different WSD methods; 2) a time evaluation of real-time WSD methods; and 3) a user study of typing clinical sentences with abbreviations using rCARD.
Results: Using 4,721 sentences containing 25 commonly observed, highly ambiguous clinical abbreviations, our evaluation showed that the best profile-based method implemented in rCARD achieved a reasonable WSD accuracy of 88.8% (comparable to SVM – 89.5%) and the cost of time for the different WSD methods are also acceptable (ranging from 0.630 to 1.649 milliseconds within the same network). The preliminary user study also showed that the extra time costs by rCARD were about 5% of total document entry time and users did not feel a significant delay when using rCARD for clinical document entry.
Conclusion: The study indicates that it is feasible to integrate a real-time, NLP-enabled abbreviation recognition and disambiguation module with clinical documentation systems.
Citation: Wu Y, Denny JC, Rosenbloom ST, Miller RA, Giuse DA, Song M, Xu H. A preliminary study of clinical abbreviation disambiguation in real time. Appl Clin Inf 2015; 6: 364–374
http://dx.doi.org/10.4338/ACI-2014-10-RA-0088
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Conflict of Interest
The authors declare that they have no competing interests.
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References
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Correspondence to:
-
References
- 1 Berman JJ. Pathology abbreviated: a long review of short terms. Arch Pathol Lab Med 2004; 128 (03) 347-352.
- 2 Stetson PD, Johnson SB, Scotch M, Hripcsak G. The sublanguage of cross-coverage. Proc AMIA Symp 2002: 742-746.
- 3 Xu H, Stetson PD, Friedman C. A study of abbreviations in clinical notes. AMIA Annu Symp Proc 2007: 821-825.
- 4 Dawson Kp Fau ’Capaldi N Capaldi N, Fau–Haydon M Haydon M, Fau–Penna AC Penna AC. The paediatric hospital medical record: a quality assessment. 19920731 DCOM- 19920731(0726–3139 (Print)).
- 5 Walsh KE, Gurwitz JH. Medical abbreviations: writing little and communicating less. Archives of disease in childhood 2008; 93 (10) 816-817.
- 6 Manzar S, Nair AK, Govind Pai M, Al-Khusaiby S. Use of abbreviations in daily progress notes. Arch Dis Child Fetal Neonatal Ed 2004; 89 (04) F374.
- 7 Sheppard JE, Weidner LC, Zakai S, Fountain-Polley S, Williams J. Ambiguous abbreviations: an audit of abbreviations in paediatric note keeping. Archives of disease in childhood 2008; 93 (03) 204-206.
- 8 ISMP Medication Safety Alert –May 2, 2001. The Institute for Safe Medication Practices; 2001.
- 9 Schuemie MJ, Kors JA, Mons B. Word sense disambiguation in the biomedical domain: an overview. J Comput Biol 2005; 12 (05) 554-565.
- 10 Xu H, Stetson PD, Friedman C. Combining corpus-derived sense profiles with estimated frequency information to disambiguate clinical abbreviations. AMIA Annu Symp Proc 2012; 2012: 1004-1013.
- 11 Pakhomov S, Pedersen T, Chute CG. Abbreviation and acronym disambiguation in clinical discourse. AMIA Annu Symp Proc 2005: 589-593.
- 12 Liu H, Teller V, Friedman C. A multi-aspect comparison study of supervised word sense disambiguation. J Am Med Inform Assoc 2000; 11 (04) 320-331.
- 13 Moon S, Pakhomov S, Melton GB. Automated disambiguation of acronyms and abbreviations in clinical texts: window and training size considerations. AMIA Annu Symp Proc 2012; 2012: 1310-1319.
- 14 Pakhomov S. Semi-supervised Maximum Entropy based approach to acronym and abbreviation normalization in medical texts. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Philadelphia, Pennsylvania: Association for Computational Linguistics 2002: 160-167.
- 15 Wu Y, Denny JC, Rosenbloom ST, Miller RA, Giuse DA, Xu H. A comparative study of current Clinical Natural Language Processing systems on handling abbreviations in discharge summaries. AMIA Annu Symp Proc 2012; 2012: 997-1003.
- 16 Weng C, Batres C, Borda T. et al. A real-time screening alert improves patient recruitment efficiency. AMIA Annu Symp Proc 2011; 2011: 1489-1498.
- 17 Filip D, Gao X, Angulo-Rodriguez L. et al. Colometer: a real-time quality feedback system for screening colonoscopy. World journal of gastroenterology: WJG 2012; 18 (32) 4270-4277.
- 18 Sandberg WS, Sandberg EH, Seim AR. et al. Real-time checking of electronic anesthesia records for documentation errors and automatically text messaging clinicians improves quality of documentation. Anesthesia and analgesia 2008; 106 (01) 192-201 table of contents.
- 19 Zhang Y, Fong S, Fiaidhi J, Mohammed S. Real-time clinical decision support system with data stream mining. Journal of biomedicine & biotechnology 2012; 2012: 580186.
- 20 Roden DM, Pulley JM, Basford MA. et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther 2008; 84 (03) 362-369.
- 21 Xu H, Wu Y, Elhadad N, Stetson PD, Friedman C. A new clustering method for detecting rare senses of abbreviations in clinical notes. J Biomed Inform 2012; 45 (06) 1075-1083.
- 22 Dawes M, Chan D. Knowing we practise good medicine: implementing the electronic medical record in family practice. Canadian family physician Medecin de famille canadien 2010; 56 (01) 15-6 e1-e3.
- 23 Westby GF. 2011 [cited 2015; Available from: http://drwes.blogspot.com/2011/01/results-are-in-age-vs-typing-speed-in.html